方法对比
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| 半监督K-均值× | K-means聚类× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2001–2002 | 1967 (formalized 1982) |
| 提出者≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | MacQueen, J. B.; Lloyd, S. P. |
| 类型≠ | Semi-supervised clustering | Partitional clustering |
| 开创性文献≠ | Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| 别名 | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| 相关≠ | 5 | 4 |
| 摘要≠ | Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full. | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. |
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